Overview

Dataset statistics

Number of variables11
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory86.1 KiB
Average record size in memory88.1 B

Variable types

NUM10
BOOL1

Warnings

X0 has unique values Unique
X1 has unique values Unique
X2 has unique values Unique
X3 has unique values Unique
X4 has unique values Unique
X5 has unique values Unique
X6 has unique values Unique
X7 has unique values Unique
X8 has unique values Unique
X9 has unique values Unique

Reproduction

Analysis started2020-12-15 21:14:39.885361
Analysis finished2020-12-15 21:15:04.064828
Duration24.18 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

X0
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04369500934
Minimum-3.110146251
Maximum3.324787536
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:04.162373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.110146251
5-th percentile-1.861543031
Q1-0.7290378174
median-0.006476288538
Q30.6317907258
95-th percentile1.670811456
Maximum3.324787536
Range6.434933787
Interquartile range (IQR)1.360828543

Descriptive statistics

Standard deviation1.043233659
Coefficient of variation (CV)-23.8753504
Kurtosis0.04291582698
Mean-0.04369500934
Median Absolute Deviation (MAD)0.68895775
Skewness-0.1064938139
Sum-43.69500934
Variance1.088336466
MonotocityNot monotonic
2020-12-15T22:15:04.374770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.257295327210.1%
 
1.36112396710.1%
 
-0.362163781710.1%
 
-0.19366092110.1%
 
0.00357769364310.1%
 
1.09556896510.1%
 
0.0633579136110.1%
 
-1.24999303710.1%
 
0.771756135910.1%
 
-0.893491401110.1%
 
0.940648657610.1%
 
-0.885671585610.1%
 
-0.380506488310.1%
 
-0.893185731210.1%
 
-2.95982775510.1%
 
-1.22379215710.1%
 
-0.242777096210.1%
 
0.444716538410.1%
 
-0.810383202510.1%
 
-0.556108777510.1%
 
-1.55260056410.1%
 
0.689573367610.1%
 
0.271545566510.1%
 
0.944726158510.1%
 
0.176163162810.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.11014625110.1%
 
-3.09878247510.1%
 
-3.07942995510.1%
 
-2.95982775510.1%
 
-2.95948761210.1%
 
-2.75673450610.1%
 
-2.69049631610.1%
 
-2.68010312710.1%
 
-2.64533683610.1%
 
-2.6093615510.1%
 
ValueCountFrequency (%) 
3.32478753610.1%
 
3.15153211610.1%
 
2.87852188210.1%
 
2.79941734110.1%
 
2.60104727310.1%
 
2.49908018410.1%
 
2.46784036910.1%
 
2.42369263210.1%
 
2.40504408310.1%
 
2.37923200110.1%
 

X1
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03146229509
Minimum-3.163973374
Maximum3.319011038
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:04.602019image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.163973374
5-th percentile-1.631650399
Q1-0.6332807217
median0.002132971569
Q30.7546836148
95-th percentile1.584264985
Maximum3.319011038
Range6.482984411
Interquartile range (IQR)1.387964337

Descriptive statistics

Standard deviation0.9977032467
Coefficient of variation (CV)31.71107651
Kurtosis-0.07652849856
Mean0.03146229509
Median Absolute Deviation (MAD)0.7016660581
Skewness0.009463666042
Sum31.46229509
Variance0.9954117685
MonotocityNot monotonic
2020-12-15T22:15:04.814020image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.0713273111510.1%
 
1.46731491810.1%
 
0.00394965835410.1%
 
0.313043867910.1%
 
0.220460296810.1%
 
-2.14336534810.1%
 
-0.57930928310.1%
 
0.728852718410.1%
 
0.222802771310.1%
 
-1.15199243710.1%
 
-0.868552637610.1%
 
-0.467256837810.1%
 
-0.417186671910.1%
 
2.50369927910.1%
 
0.541329738210.1%
 
-0.467396604810.1%
 
-0.420059546810.1%
 
-1.26924027910.1%
 
1.92510894910.1%
 
0.0722280518410.1%
 
1.00563614810.1%
 
-0.533275780110.1%
 
-0.0813909977610.1%
 
0.241770207410.1%
 
-2.4880597310.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.16397337410.1%
 
-2.76790027510.1%
 
-2.68955086410.1%
 
-2.55116996110.1%
 
-2.4880597310.1%
 
-2.42188671410.1%
 
-2.39027297310.1%
 
-2.36640972710.1%
 
-2.28822687710.1%
 
-2.28289713610.1%
 
ValueCountFrequency (%) 
3.31901103810.1%
 
2.99318725810.1%
 
2.83338080710.1%
 
2.57061262510.1%
 
2.54123672310.1%
 
2.52106943410.1%
 
2.50714485410.1%
 
2.50369927910.1%
 
2.46568571210.1%
 
2.46040834910.1%
 

X2
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.02904200563
Minimum-2.944290813
Maximum3.543095479
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:05.040666image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.944290813
5-th percentile-1.619745128
Q1-0.6483471708
median-0.003956663927
Q30.7206954687
95-th percentile1.741467541
Maximum3.543095479
Range6.487386292
Interquartile range (IQR)1.369042639

Descriptive statistics

Standard deviation1.018583489
Coefficient of variation (CV)35.07276672
Kurtosis-0.03088743933
Mean0.02904200563
Median Absolute Deviation (MAD)0.6883168213
Skewness0.1075120747
Sum29.04200563
Variance1.037512323
MonotocityNot monotonic
2020-12-15T22:15:05.252604image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.54245451510.1%
 
-1.02706290110.1%
 
-0.328722166510.1%
 
0.78632458610.1%
 
0.151928266710.1%
 
0.752293799510.1%
 
0.291049723310.1%
 
2.07449448310.1%
 
1.1985313610.1%
 
-0.477524063710.1%
 
0.318493289910.1%
 
-0.386929266110.1%
 
-0.161276843510.1%
 
-0.157418440310.1%
 
-1.17926766110.1%
 
-0.998451621310.1%
 
-0.154771111710.1%
 
-1.9207720710.1%
 
1.43568025810.1%
 
-0.999383848810.1%
 
0.625259593410.1%
 
-1.4552476610.1%
 
-0.137168319710.1%
 
0.478292008910.1%
 
2.82116000210.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.94429081310.1%
 
-2.84193281110.1%
 
-2.60066325710.1%
 
-2.54372868710.1%
 
-2.40845706910.1%
 
-2.37988771310.1%
 
-2.34259310.1%
 
-2.31406460310.1%
 
-2.28630070910.1%
 
-2.27980966310.1%
 
ValueCountFrequency (%) 
3.54309547910.1%
 
3.54245451510.1%
 
2.82116000210.1%
 
2.64175662410.1%
 
2.5876987110.1%
 
2.57353784810.1%
 
2.56881727910.1%
 
2.52651576610.1%
 
2.5249430910.1%
 
2.52000413710.1%
 

X3
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04800715778
Minimum-3.039933901
Maximum3.064525383
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:05.478708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.039933901
5-th percentile-1.603640223
Q1-0.6489592521
median0.06890844925
Q30.7270156526
95-th percentile1.700454675
Maximum3.064525383
Range6.104459284
Interquartile range (IQR)1.375974905

Descriptive statistics

Standard deviation1.001904631
Coefficient of variation (CV)20.86990101
Kurtosis-0.13398441
Mean0.04800715778
Median Absolute Deviation (MAD)0.7002552961
Skewness-0.04562762625
Sum48.00715778
Variance1.003812889
MonotocityNot monotonic
2020-12-15T22:15:05.686969image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.61140540310.1%
 
1.5086098710.1%
 
-0.130066566110.1%
 
0.466612655510.1%
 
0.690119609810.1%
 
-2.16036339910.1%
 
0.423460483710.1%
 
0.44963963410.1%
 
-0.385273710310.1%
 
0.144913925510.1%
 
-1.05164734910.1%
 
-0.0170715313310.1%
 
0.399866309110.1%
 
-0.239027839510.1%
 
-0.0934055128610.1%
 
-1.38787860110.1%
 
0.260057996710.1%
 
1.5974655410.1%
 
-0.43720169510.1%
 
0.40597312110.1%
 
-0.744662433810.1%
 
-0.0597203626710.1%
 
1.11442752310.1%
 
1.2598966710.1%
 
0.580404696110.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.03993390110.1%
 
-2.97901332810.1%
 
-2.76436048410.1%
 
-2.69725456910.1%
 
-2.6605609310.1%
 
-2.51408100810.1%
 
-2.45903326610.1%
 
-2.45333095910.1%
 
-2.38602938810.1%
 
-2.38581128810.1%
 
ValueCountFrequency (%) 
3.06452538310.1%
 
2.92494199510.1%
 
2.70411794810.1%
 
2.64089893810.1%
 
2.62443354410.1%
 
2.59713501610.1%
 
2.57549860710.1%
 
2.49382528810.1%
 
2.45239701310.1%
 
2.33391629210.1%
 

X4
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.02906861017
Minimum-3.125371951
Maximum3.379665667
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:05.912679image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.125371951
5-th percentile-1.70234405
Q1-0.6852044075
median-0.04334099348
Q30.6498461627
95-th percentile1.578715812
Maximum3.379665667
Range6.505037618
Interquartile range (IQR)1.33505057

Descriptive statistics

Standard deviation1.017185553
Coefficient of variation (CV)-34.9925761
Kurtosis0.07034382378
Mean-0.02906861017
Median Absolute Deviation (MAD)0.6740211674
Skewness-0.04067772122
Sum-29.06861017
Variance1.03466645
MonotocityNot monotonic
2020-12-15T22:15:06.266597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.34151198710.1%
 
-0.0592028312110.1%
 
-0.798811158610.1%
 
0.515860403510.1%
 
0.529390866710.1%
 
-3.11124835610.1%
 
0.332925957910.1%
 
0.874281467510.1%
 
0.591933566810.1%
 
-0.967547469410.1%
 
-0.412683090210.1%
 
0.43551116110.1%
 
0.922364619710.1%
 
-0.799322531410.1%
 
-1.8258100710.1%
 
0.923191033710.1%
 
1.12774077610.1%
 
-1.43711063910.1%
 
-0.306580200910.1%
 
0.425096339310.1%
 
1.43403847710.1%
 
-1.49254104410.1%
 
-1.5255882210.1%
 
-0.434792223210.1%
 
-0.846679530910.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.12537195110.1%
 
-3.11124835610.1%
 
-3.06209668210.1%
 
-3.02987211110.1%
 
-3.02606896110.1%
 
-2.73936089910.1%
 
-2.5816729610.1%
 
-2.56790308310.1%
 
-2.51887383310.1%
 
-2.47691053710.1%
 
ValueCountFrequency (%) 
3.37966566710.1%
 
2.8747566410.1%
 
2.83114971510.1%
 
2.81799278210.1%
 
2.66828124610.1%
 
2.6607741310.1%
 
2.63675399210.1%
 
2.54691610710.1%
 
2.49593114110.1%
 
2.48652791810.1%
 

X5
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03586685831
Minimum-2.943556286
Maximum2.92121433
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:06.490523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.943556286
5-th percentile-1.628623814
Q1-0.6175158991
median0.04313800529
Q30.7398361126
95-th percentile1.636447799
Maximum2.92121433
Range5.864770616
Interquartile range (IQR)1.357352012

Descriptive statistics

Standard deviation1.003449272
Coefficient of variation (CV)27.9770607
Kurtosis-0.1725425925
Mean0.03586685831
Median Absolute Deviation (MAD)0.6808224101
Skewness-0.09753286359
Sum35.86685831
Variance1.006910441
MonotocityNot monotonic
2020-12-15T22:15:06.696513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-2.79210412710.1%
 
0.539705364510.1%
 
0.699520865810.1%
 
-2.10551986110.1%
 
-0.35251120310.1%
 
0.919959618810.1%
 
-1.31539060310.1%
 
-2.94355628610.1%
 
-0.624205289810.1%
 
0.308481740710.1%
 
-0.737254015710.1%
 
-0.883372624310.1%
 
-2.1891774510.1%
 
-0.216128509310.1%
 
-1.28172612510.1%
 
-0.193252186510.1%
 
-0.763265136610.1%
 
-2.13712371810.1%
 
-0.227017400810.1%
 
1.54417569310.1%
 
0.296795195210.1%
 
-0.501123119910.1%
 
-0.0206779814510.1%
 
-0.179489412810.1%
 
-0.0281322010410.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.94355628610.1%
 
-2.79210412710.1%
 
-2.76521574310.1%
 
-2.76101951710.1%
 
-2.70076574810.1%
 
-2.65409276810.1%
 
-2.56754486710.1%
 
-2.50744620210.1%
 
-2.4762838110.1%
 
-2.39728190410.1%
 
ValueCountFrequency (%) 
2.9212143310.1%
 
2.87125984210.1%
 
2.77013361810.1%
 
2.6716649210.1%
 
2.5340141710.1%
 
2.51122273610.1%
 
2.49262641810.1%
 
2.46972039410.1%
 
2.39334394710.1%
 
2.16341028710.1%
 

X6
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.006968467593
Minimum-3.357616091
Maximum2.833772473
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:06.920055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.357616091
5-th percentile-1.571710046
Q1-0.6554668139
median0.0009903445529
Q30.6434985966
95-th percentile1.620520548
Maximum2.833772473
Range6.191388564
Interquartile range (IQR)1.298965411

Descriptive statistics

Standard deviation0.9742029692
Coefficient of variation (CV)139.8016072
Kurtosis0.1374163925
Mean0.006968467593
Median Absolute Deviation (MAD)0.652910317
Skewness-0.001285113004
Sum6.968467593
Variance0.9490714251
MonotocityNot monotonic
2020-12-15T22:15:07.131682image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-1.47152338910.1%
 
-0.379258505110.1%
 
-1.05742803310.1%
 
-0.453283414810.1%
 
-0.369624055610.1%
 
-0.981714589710.1%
 
-1.59360566610.1%
 
0.4220798310.1%
 
-0.0342130685510.1%
 
0.583926648710.1%
 
-3.35761609110.1%
 
-0.0586069048310.1%
 
0.892631447510.1%
 
-0.918853609110.1%
 
0.671688425810.1%
 
1.94604682610.1%
 
0.516347804810.1%
 
0.208719036910.1%
 
1.05048322210.1%
 
0.0736299308410.1%
 
-1.00693965310.1%
 
-0.0940020165810.1%
 
-0.915157446510.1%
 
-0.17217592810.1%
 
0.805130665610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.35761609110.1%
 
-2.94258015710.1%
 
-2.91652262210.1%
 
-2.72746041910.1%
 
-2.62712004510.1%
 
-2.57137100310.1%
 
-2.52555448210.1%
 
-2.38868971910.1%
 
-2.3562116610.1%
 
-2.34811161610.1%
 
ValueCountFrequency (%) 
2.83377247310.1%
 
2.78758921510.1%
 
2.78290386110.1%
 
2.74701302610.1%
 
2.61518816210.1%
 
2.58852810810.1%
 
2.5450011510.1%
 
2.53249856210.1%
 
2.5192441810.1%
 
2.48043979910.1%
 

X7
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01510057469
Minimum-3.312670831
Maximum3.867935141
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:07.363942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.312670831
5-th percentile-1.752676775
Q1-0.629321937
median0.01122562809
Q30.7201833262
95-th percentile1.668681898
Maximum3.867935141
Range7.180605972
Interquartile range (IQR)1.349505263

Descriptive statistics

Standard deviation1.020910989
Coefficient of variation (CV)67.6074262
Kurtosis0.08993940935
Mean0.01510057469
Median Absolute Deviation (MAD)0.6878075124
Skewness-0.01968158048
Sum15.10057469
Variance1.042259247
MonotocityNot monotonic
2020-12-15T22:15:07.583519image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-0.276161634710.1%
 
-0.584179198810.1%
 
-1.42860051910.1%
 
0.318505044210.1%
 
0.65692696510.1%
 
-0.232792342310.1%
 
1.78706829310.1%
 
-0.466344094910.1%
 
0.0144199582710.1%
 
-1.01674561110.1%
 
1.58474366510.1%
 
1.39835768910.1%
 
1.06802364910.1%
 
1.11317825710.1%
 
0.558524262110.1%
 
1.25799280710.1%
 
-0.0798140033510.1%
 
-0.398299060210.1%
 
-0.805563774310.1%
 
-1.03771177310.1%
 
1.93921252710.1%
 
-1.22728397510.1%
 
-0.293536081210.1%
 
0.638309474310.1%
 
-1.06659820610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.31267083110.1%
 
-2.99616597210.1%
 
-2.70151287610.1%
 
-2.65024864810.1%
 
-2.53844136810.1%
 
-2.41365682510.1%
 
-2.41348701610.1%
 
-2.39589778910.1%
 
-2.35309038610.1%
 
-2.3431692210.1%
 
ValueCountFrequency (%) 
3.86793514110.1%
 
3.61920290710.1%
 
3.36105290110.1%
 
2.85748052410.1%
 
2.52747629610.1%
 
2.50913304710.1%
 
2.46674524710.1%
 
2.38062819510.1%
 
2.37799689210.1%
 
2.34231069910.1%
 

X8
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.04387794625
Minimum-3.292282968
Maximum3.082503657
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:07.814219image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.292282968
5-th percentile-1.77767981
Q1-0.748052042
median-0.01097161998
Q30.6897635011
95-th percentile1.601054535
Maximum3.082503657
Range6.374786625
Interquartile range (IQR)1.437815543

Descriptive statistics

Standard deviation1.037798365
Coefficient of variation (CV)-23.65193574
Kurtosis-0.05914217212
Mean-0.04387794625
Median Absolute Deviation (MAD)0.7224623185
Skewness-0.1009984901
Sum-43.87794625
Variance1.077025447
MonotocityNot monotonic
2020-12-15T22:15:08.022513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.7873889510.1%
 
-0.61165601210.1%
 
1.12869804510.1%
 
2.10531188510.1%
 
0.185049909910.1%
 
-0.600609357210.1%
 
1.09996138510.1%
 
1.44012794210.1%
 
-0.206366146710.1%
 
-1.54803935210.1%
 
1.41622549710.1%
 
0.360140530810.1%
 
0.0156628105710.1%
 
-2.64028070710.1%
 
-0.972501937710.1%
 
0.829991755210.1%
 
0.520428411410.1%
 
1.78704149910.1%
 
0.72250497810.1%
 
-0.853264578810.1%
 
1.17631578810.1%
 
-0.260076041310.1%
 
-0.557365839510.1%
 
1.75593460310.1%
 
1.0602208510.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-3.29228296810.1%
 
-3.12630112410.1%
 
-3.04077439110.1%
 
-2.96839037510.1%
 
-2.96066753910.1%
 
-2.81406179810.1%
 
-2.80075034510.1%
 
-2.64028070710.1%
 
-2.62934423310.1%
 
-2.59064489810.1%
 
ValueCountFrequency (%) 
3.08250365710.1%
 
3.01310445810.1%
 
2.75534739610.1%
 
2.66898022910.1%
 
2.5498340210.1%
 
2.47365572210.1%
 
2.36952928810.1%
 
2.33738842310.1%
 
2.29590737210.1%
 
2.24729555110.1%
 

X9
Real number (ℝ)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05050100444
Minimum-2.810451722
Maximum3.377959107
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2020-12-15T22:15:08.246063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-2.810451722
5-th percentile-1.584511032
Q1-0.6205298473
median0.1022794287
Q30.7425893161
95-th percentile1.629297049
Maximum3.377959107
Range6.188410829
Interquartile range (IQR)1.363119163

Descriptive statistics

Standard deviation1.000733802
Coefficient of variation (CV)19.81611679
Kurtosis-0.1422145247
Mean0.05050100444
Median Absolute Deviation (MAD)0.6769368472
Skewness-0.06879551653
Sum50.50100444
Variance1.001468142
MonotocityNot monotonic
2020-12-15T22:15:08.457998image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.0718983269110.1%
 
0.681830012910.1%
 
1.20296203310.1%
 
-0.27578022710.1%
 
0.554799504310.1%
 
-0.222342724910.1%
 
-0.805762243610.1%
 
-0.542166210410.1%
 
-1.98287806810.1%
 
-0.0463714924910.1%
 
-1.62318009710.1%
 
-0.267594968510.1%
 
-0.295211895310.1%
 
0.595730711910.1%
 
-0.697421180910.1%
 
0.524586947210.1%
 
-0.346729752910.1%
 
1.10015302310.1%
 
-2.17758842210.1%
 
0.978688103110.1%
 
0.146305059210.1%
 
-0.258081082910.1%
 
1.58246975210.1%
 
-1.49542907610.1%
 
0.130860348610.1%
 
Other values (975)97597.5%
 
ValueCountFrequency (%) 
-2.81045172210.1%
 
-2.69048881510.1%
 
-2.66257758210.1%
 
-2.58466915110.1%
 
-2.50394915910.1%
 
-2.44047260810.1%
 
-2.42034660910.1%
 
-2.37722656910.1%
 
-2.37254592610.1%
 
-2.32999473910.1%
 
ValueCountFrequency (%) 
3.37795910710.1%
 
2.97348250610.1%
 
2.92156196210.1%
 
2.82714658210.1%
 
2.61510011710.1%
 
2.50455589810.1%
 
2.492368510.1%
 
2.48331637710.1%
 
2.45058499810.1%
 
2.31613703310.1%
 

y
Boolean

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.9 KiB
1
500 
0
500 
ValueCountFrequency (%) 
150050.0%
 
050050.0%
 
2020-12-15T22:15:08.614732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2020-12-15T22:14:40.849035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:41.066288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:41.283264image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:41.500537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:41.718625image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:41.936140image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:42.153089image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:42.514248image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:42.734347image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:42.948995image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:43.164114image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:43.378451image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:43.594110image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:43.808986image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:44.024193image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:44.240338image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:44.455408image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:44.669941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:44.890461image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:45.104119image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:45.319040image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:45.531950image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:45.746281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:45.961113image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:46.175373image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:46.391261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:46.605260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:46.821032image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:47.043343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:47.260808image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:47.479320image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:47.696867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:47.914399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:48.129055image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:48.345276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:48.564504image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:48.923539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:49.139315image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:49.361276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:49.578732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:49.795039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:50.010839image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:50.227479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:50.443281image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:50.660445image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:50.873812image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:51.088708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:51.304349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:51.525498image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:51.740433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:51.955793image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:52.178477image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:52.394946image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:52.610586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:52.830632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:53.047798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:53.265300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:53.480608image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:53.700414image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:53.916162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:54.132571image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:54.351063image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:54.566771image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:54.783469image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:55.001053image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:55.390591image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:55.608350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:55.826310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:56.049805image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:56.267143image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:56.482633image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:56.703274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:56.926861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:57.150947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:57.373861image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:57.594099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:57.817045image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:58.041983image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:58.269433image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:58.492100image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:58.711942image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:58.927036image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:59.139691image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:59.356030image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:59.575286image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:14:59.794202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:00.010787image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:00.227546image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:00.449161image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:00.665458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:01.039190image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:01.293274image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:01.509786image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:01.727568image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:02.098925image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:02.315009image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:02.532481image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:02.750177image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:02.972943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:03.187343image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-15T22:15:08.732434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-15T22:15:09.021198image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-15T22:15:09.462850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-15T22:15:09.755237image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-12-15T22:15:03.557770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-15T22:15:03.930434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

X0X1X2X3X4X5X6X7X8X9y
0-1.591679-1.9447390.2209310.0525300.9752510.811792-0.549148-0.2327921.972032-1.0089640
10.064400-0.6681111.924775-0.6728001.118917-1.081578-1.907725-1.1304790.317234-1.5247481
21.1032101.6992041.634633-1.430127-0.108652-0.777151-0.305163-1.375696-0.116328-0.1143720
3-0.267161-0.353253-0.7185550.351178-0.292465-0.175475-1.632129-0.247423-0.8827330.6476201
4-0.345546-0.814074-0.8764140.2744130.867676-0.8128670.8235870.697439-0.5516440.1602190
5-1.861444-0.3408930.094555-1.1874320.163624-0.1853470.8100460.4440090.397058-0.9630931
62.4990800.771034-0.511532-0.5248141.025351-0.835185-0.444607-0.3668660.7531441.9029001
70.9062700.3536890.4772632.6244341.1966430.7998590.9702431.0667240.813053-0.5742200
81.673484-0.4361671.2009201.041947-1.747506-0.0397140.219542-0.5619781.4325270.9092621
90.599455-1.369297-2.236841-0.449905-1.025310-0.501123-0.9450020.706046-0.8038780.3345331

Last rows

X0X1X2X3X4X5X6X7X8X9y
9900.1911861.179568-0.283563-0.027366-0.242133-0.7372540.132974-0.4019360.0603400.9439590
9910.1931892.1435101.2638840.7921360.445083-1.092026-0.306610-0.707648-2.814062-0.4345700
9921.1334860.5838310.042270-0.1776460.395026-1.4584490.0789501.7042442.037975-0.9436611
9931.253203-1.656053-0.444813-1.558047-0.450096-0.0064000.7345520.148219-2.070996-0.0434421
994-0.361847-0.043520-0.050744-0.7571501.330672-0.8799570.930551-0.8626050.1230691.1925700
9951.4813190.1748860.6403161.8871051.0557510.981368-0.5493891.266495-0.156182-0.0305831
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